We present a new methodology to statistically determine the net present value(NPV)and internal rate of return(IRR)as financial estimators of shale gas investments.Our method allows us to forecast,in a fully probabilis...We present a new methodology to statistically determine the net present value(NPV)and internal rate of return(IRR)as financial estimators of shale gas investments.Our method allows us to forecast,in a fully probabilistic setting,financial performance risk and to understand the importance of the different factors that impact investment.The methodology developed in this study combines,through Monte Carlo simulation,the computational modeling of gas production from shale gas wells with a stochastic simulation of gas price as a geometric Brownian motion(GMB).To illustrate the methodology's validity,we apply it to an analysis of investments in shale gas wells.Our results show that gas price volatility is a key variable in the performance of an investment of this type,in such a way that at high volatilities,the potential return on an investment in shale gas increases significantly,but so do the risks of economic loss.This finding is consistent with the history of shale gas operations in which huge investment successes coexist with unexpected investment failures.展开更多
A new stochastic volatility（SV）method to estimate the conditional value at risk（CVaR）is put forward.Firstly,it makes use of SV model to forecast the volatility of return.Secondly,the Markov chain Monte Carlo（MCMC...A new stochastic volatility（SV）method to estimate the conditional value at risk（CVaR）is put forward.Firstly,it makes use of SV model to forecast the volatility of return.Secondly,the Markov chain Monte Carlo（MCMC）simulation and Gibbs sampling have been used to estimate the parameters in the SV model.Thirdly,in this model,CVaR calculation is immediate.In this way,the SV-CVaR model overcomes the drawbacks of the generalized autoregressive conditional heteroscedasticity value at risk（GARCH-VaR）model.Empirical study suggests that this model is better than GARCH-VaR model in this field.展开更多
基金partially funded by Goverment of Spain,Ministry of Science,Innovation and Universities(grant:RTI2018093366-B-I00)by Goverment of Spain,Ministry of Universities(grant:Subsidies to Public Universities for the Requalification of the Spanish University System,“Margarita Salas”Grants Modality for the Training of Young Doctors,RD 289/2021 of April 20)+1 种基金by the Xunta de Galicia,Consellería de Educacion e Ordenación Universitaria(grant:#ED431C 2018/41)by the Group of Numerical Methods in Engineering of the Universidade de A Coruna。
文摘We present a new methodology to statistically determine the net present value(NPV)and internal rate of return(IRR)as financial estimators of shale gas investments.Our method allows us to forecast,in a fully probabilistic setting,financial performance risk and to understand the importance of the different factors that impact investment.The methodology developed in this study combines,through Monte Carlo simulation,the computational modeling of gas production from shale gas wells with a stochastic simulation of gas price as a geometric Brownian motion(GMB).To illustrate the methodology's validity,we apply it to an analysis of investments in shale gas wells.Our results show that gas price volatility is a key variable in the performance of an investment of this type,in such a way that at high volatilities,the potential return on an investment in shale gas increases significantly,but so do the risks of economic loss.This finding is consistent with the history of shale gas operations in which huge investment successes coexist with unexpected investment failures.
基金Sponsored by the National Natural Science Foundation of China(70571010)
文摘A new stochastic volatility（SV）method to estimate the conditional value at risk（CVaR）is put forward.Firstly,it makes use of SV model to forecast the volatility of return.Secondly,the Markov chain Monte Carlo（MCMC）simulation and Gibbs sampling have been used to estimate the parameters in the SV model.Thirdly,in this model,CVaR calculation is immediate.In this way,the SV-CVaR model overcomes the drawbacks of the generalized autoregressive conditional heteroscedasticity value at risk（GARCH-VaR）model.Empirical study suggests that this model is better than GARCH-VaR model in this field.
基金Supported by“Chen Guang”Project of Shanghai Municipal Education Commission(09CG67)Shanghai Education Research and Innovation Project(10YS187)2009 Shanghai Financial University Research Project(SHFUKT09-12)
